Data-driven brain-types and their cognitive consequences

The canonical approach to exploring brain-behaviour relationships is to group individuals according to a phenotype of interest, and then explore the neural correlates of this grouping. A limitation of this approach is that multiple aetiological pathways could result in the phenotype of interest, so the role of any one brain mechanism may be substantially underestimated. We show that recent advances in network analysis make it possible to group individuals at a neural level - to identify subgroups of individuals with similarly organized brains. Across three independent samples (total N = 313, mean age: 11.24 years, range: 5-21 years) we used a data-driven community clustering algorithm to identify robust subgroups using white-matter microstructure organization in childhood and adolescence. The algorithm indicated the presence of two groups of roughly equal sizes. A critical organizational difference between the groups was the role of the left and right cingulum. These different brain types had profoundly different cognitive abilities: Groups with higher FA in the cingulum performed better across assessments of fluid intelligence, vocabulary, verbal and visuospatial short-term and working memory, and longer-term memory. We next explored the potential mechanistic role of the cingulum. A connectomics analysis indicated reduced anterior-posterior structural connectivity in the low cingulum FA subgroup. We then used resting-state functional data from the same individuals and showed that cingulum FA was strongly related to activation of the default mode network. In summary, inter-individual differences in cingulum microstructural organisation allowed for biologically-based grouping, which has a dramatic effect on cognition and the functional activation of the default mode network. Using this new approach, we propose that the cingulum plays a key role in the integration of cortical areas, which is pivotal for cognitive ability in children and young people.